Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations158700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.0 MiB
Average record size in memory634.3 B

Variable types

Numeric6
Text3
Categorical5

Alerts

agree_pct is highly overall correlated with benchmarkHigh correlation
agree_pct_sd is highly overall correlated with population and 1 other fieldsHigh correlation
benchmark is highly overall correlated with agree_pctHigh correlation
cah1_code is highly overall correlated with cah1_subject and 2 other fieldsHigh correlation
cah1_subject is highly overall correlated with cah1_code and 2 other fieldsHigh correlation
cah2_code is highly overall correlated with cah1_code and 2 other fieldsHigh correlation
cah2_subject is highly overall correlated with cah1_code and 2 other fieldsHigh correlation
population is highly overall correlated with agree_pct_sd and 1 other fieldsHigh correlation
respondents is highly overall correlated with agree_pct_sd and 1 other fieldsHigh correlation
ukprn is highly skewed (γ1 = 38.42856175)Skewed

Reproduction

Analysis started2025-12-03 16:22:11.239789
Analysis finished2025-12-03 16:22:13.682893
Duration2.44 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

ukprn
Real number (ℝ)

Skewed 

Distinct374
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10067643
Minimum10000163
Maximum99999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:13.702643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10000163
5-th percentile10001143
Q110005500
median10007159
Q310007796
95-th percentile10014001
Maximum99999999
Range89999836
Interquartile range (IQR)2296

Descriptive statistics

Standard deviation2284445
Coefficient of variation (CV)0.22690962
Kurtosis1477.9627
Mean10067643
Median Absolute Deviation (MAD)642
Skewness38.428562
Sum1.5977349 × 1012
Variance5.218689 × 1012
MonotonicityNot monotonic
2025-12-03T16:22:13.769328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100077952349
 
1.5%
100077982187
 
1.4%
100071542025
 
1.3%
100077901944
 
1.2%
100077941836
 
1.2%
100077841782
 
1.1%
100047971728
 
1.1%
100071561728
 
1.1%
100041801701
 
1.1%
100068401674
 
1.1%
Other values (364)139746
88.1%
ValueCountFrequency (%)
1000016354
 
< 0.1%
100002911242
0.8%
1000038154
 
< 0.1%
10000385243
 
0.2%
1000041581
 
0.1%
1000053327
 
< 0.1%
1000053681
 
0.1%
1000061054
 
< 0.1%
10000712486
 
0.3%
1000072154
 
< 0.1%
ValueCountFrequency (%)
9999999927
 
< 0.1%
9999999827
 
< 0.1%
9999999727
 
< 0.1%
9000045127
 
< 0.1%
1009462754
 
< 0.1%
1008959127
 
< 0.1%
10089266135
0.1%
1008853954
 
< 0.1%
1008832127
 
< 0.1%
1008717054
 
< 0.1%
Distinct374
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-12-03T16:22:13.865379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length63
Median length50
Mean length25.717549
Min length3

Characters and Unicode

Total characters4081375
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAP Education Limited
2nd rowAAP Education Limited
3rd rowAAP Education Limited
4th rowAAP Education Limited
5th rowAAP Education Limited
ValueCountFrequency (%)
university138128
24.9%
of95714
17.3%
the38367
 
6.9%
london13554
 
2.4%
college13067
 
2.4%
limited5452
 
1.0%
and4887
 
0.9%
manchester4779
 
0.9%
leeds4401
 
0.8%
liverpool4347
 
0.8%
Other values (450)231624
41.8%
2025-12-03T16:22:13.992924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
395620
 
9.7%
i387084
 
9.5%
e386439
 
9.5%
n276744
 
6.8%
t275391
 
6.7%
r274122
 
6.7%
o263597
 
6.5%
s230329
 
5.6%
y166639
 
4.1%
v147173
 
3.6%
Other values (51)1278237
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4081375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
395620
 
9.7%
i387084
 
9.5%
e386439
 
9.5%
n276744
 
6.8%
t275391
 
6.7%
r274122
 
6.7%
o263597
 
6.5%
s230329
 
5.6%
y166639
 
4.1%
v147173
 
3.6%
Other values (51)1278237
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4081375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
395620
 
9.7%
i387084
 
9.5%
e386439
 
9.5%
n276744
 
6.8%
t275391
 
6.7%
r274122
 
6.7%
o263597
 
6.5%
s230329
 
5.6%
y166639
 
4.1%
v147173
 
3.6%
Other values (51)1278237
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4081375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
395620
 
9.7%
i387084
 
9.5%
e386439
 
9.5%
n276744
 
6.8%
t275391
 
6.7%
r274122
 
6.7%
o263597
 
6.5%
s230329
 
5.6%
y166639
 
4.1%
v147173
 
3.6%
Other values (51)1278237
31.3%

cah1_code
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
CAH02
22272 
CAH17
19764 
CAH15
15147 
CAH25
14337 
CAH10
11447 
Other values (16)
75733 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters793500
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAH11
2nd rowCAH11
3rd rowCAH11
4th rowCAH11
5th rowCAH11

Common Values

ValueCountFrequency (%)
CAH0222272
14.0%
CAH1719764
12.5%
CAH1515147
9.5%
CAH2514337
9.0%
CAH1011447
 
7.2%
CAH1910044
 
6.3%
CAH119881
 
6.2%
CAH039828
 
6.2%
CAH206912
 
4.4%
CAH045400
 
3.4%
Other values (11)33668
21.2%

Length

2025-12-03T16:22:14.016588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cah0222272
14.0%
cah1719764
12.5%
cah1515147
9.5%
cah2514337
9.0%
cah1011447
 
7.2%
cah1910044
 
6.3%
cah119881
 
6.2%
cah039828
 
6.2%
cah206912
 
4.4%
cah045400
 
3.4%
Other values (11)33668
21.2%

Most occurring characters

ValueCountFrequency (%)
C158700
20.0%
A158700
20.0%
H158700
20.0%
184723
10.7%
067874
8.6%
263256
 
8.0%
530213
 
3.8%
724840
 
3.1%
314148
 
1.8%
912501
 
1.6%
Other values (2)19845
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)793500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C158700
20.0%
A158700
20.0%
H158700
20.0%
184723
10.7%
067874
8.6%
263256
 
8.0%
530213
 
3.8%
724840
 
3.1%
314148
 
1.8%
912501
 
1.6%
Other values (2)19845
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)793500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C158700
20.0%
A158700
20.0%
H158700
20.0%
184723
10.7%
067874
8.6%
263256
 
8.0%
530213
 
3.8%
724840
 
3.1%
314148
 
1.8%
912501
 
1.6%
Other values (2)19845
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)793500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C158700
20.0%
A158700
20.0%
H158700
20.0%
184723
10.7%
067874
8.6%
263256
 
8.0%
530213
 
3.8%
724840
 
3.1%
314148
 
1.8%
912501
 
1.6%
Other values (2)19845
 
2.5%

cah1_subject
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
subjects allied to medicine
22272 
business and management
19764 
social sciences
15147 
design, and creative and performing arts
14337 
engineering and technology
11447 
Other values (16)
75733 

Length

Max length47
Median length36
Mean length25.452628
Min length3

Characters and Unicode

Total characters4039332
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomputing
2nd rowcomputing
3rd rowcomputing
4th rowcomputing
5th rowcomputing

Common Values

ValueCountFrequency (%)
subjects allied to medicine22272
14.0%
business and management19764
12.5%
social sciences15147
9.5%
design, and creative and performing arts14337
9.0%
engineering and technology11447
 
7.2%
language and area studies10044
 
6.3%
computing9881
 
6.2%
biological and sport sciences9828
 
6.2%
historical, philosophical and religious studies6912
 
4.4%
psychology5400
 
3.4%
Other values (11)33668
21.2%

Length

2025-12-03T16:22:14.037332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and108727
20.3%
sciences33237
 
6.2%
studies24516
 
4.6%
medicine24216
 
4.5%
subjects22272
 
4.2%
allied22272
 
4.2%
to22272
 
4.2%
management19764
 
3.7%
business19764
 
3.7%
social15147
 
2.8%
Other values (36)224149
41.8%

Most occurring characters

ValueCountFrequency (%)
e427149
10.6%
377636
 
9.3%
n374964
 
9.3%
i358870
 
8.9%
a343163
 
8.5%
s315327
 
7.8%
c228329
 
5.7%
d213237
 
5.3%
t198629
 
4.9%
o184997
 
4.6%
Other values (14)1017031
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4039332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e427149
10.6%
377636
 
9.3%
n374964
 
9.3%
i358870
 
8.9%
a343163
 
8.5%
s315327
 
7.8%
c228329
 
5.7%
d213237
 
5.3%
t198629
 
4.9%
o184997
 
4.6%
Other values (14)1017031
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4039332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e427149
10.6%
377636
 
9.3%
n374964
 
9.3%
i358870
 
8.9%
a343163
 
8.5%
s315327
 
7.8%
c228329
 
5.7%
d213237
 
5.3%
t198629
 
4.9%
o184997
 
4.6%
Other values (14)1017031
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4039332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e427149
10.6%
377636
 
9.3%
n374964
 
9.3%
i358870
 
8.9%
a343163
 
8.5%
s315327
 
7.8%
c228329
 
5.7%
d213237
 
5.3%
t198629
 
4.9%
o184997
 
4.6%
Other values (14)1017031
25.2%

cah2_code
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
CAH17-01
19764 
CAH10-01
 
9962
CAH11-01
 
9881
CAH02-04
 
8666
CAH02-06
 
7666
Other values (30)
102761 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1269600
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAH11-01
2nd rowCAH11-01
3rd rowCAH11-01
4th rowCAH11-01
5th rowCAH11-01

Common Values

ValueCountFrequency (%)
CAH17-0119764
 
12.5%
CAH10-019962
 
6.3%
CAH11-019881
 
6.2%
CAH02-048666
 
5.5%
CAH02-067666
 
4.8%
CAH25-017641
 
4.8%
CAH03-017047
 
4.4%
CAH25-026696
 
4.2%
CAH19-016048
 
3.8%
CAH15-015643
 
3.6%
Other values (25)69686
43.9%

Length

2025-12-03T16:22:14.057539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cah17-0119764
 
12.5%
cah10-019962
 
6.3%
cah11-019881
 
6.2%
cah02-048666
 
5.5%
cah02-067666
 
4.8%
cah25-017641
 
4.8%
cah03-017047
 
4.4%
cah25-026696
 
4.2%
cah19-016048
 
3.8%
cah15-015643
 
3.6%
Other values (25)69686
43.9%

Most occurring characters

ValueCountFrequency (%)
0226574
17.8%
1191316
15.1%
C158700
12.5%
A158700
12.5%
H158700
12.5%
-158700
12.5%
280752
 
6.4%
534344
 
2.7%
429159
 
2.3%
724840
 
2.0%
Other values (3)47815
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1269600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0226574
17.8%
1191316
15.1%
C158700
12.5%
A158700
12.5%
H158700
12.5%
-158700
12.5%
280752
 
6.4%
534344
 
2.7%
429159
 
2.3%
724840
 
2.0%
Other values (3)47815
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1269600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0226574
17.8%
1191316
15.1%
C158700
12.5%
A158700
12.5%
H158700
12.5%
-158700
12.5%
280752
 
6.4%
534344
 
2.7%
429159
 
2.3%
724840
 
2.0%
Other values (3)47815
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1269600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0226574
17.8%
1191316
15.1%
C158700
12.5%
A158700
12.5%
H158700
12.5%
-158700
12.5%
280752
 
6.4%
534344
 
2.7%
429159
 
2.3%
724840
 
2.0%
Other values (3)47815
 
3.8%

cah2_subject
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
business and management
19764 
engineering
 
9962
computing
 
9881
nursing and midwifery
 
8666
allied health
 
7666
Other values (30)
102761 

Length

Max length42
Median length36
Mean length20.534921
Min length3

Characters and Unicode

Total characters3258892
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomputing
2nd rowcomputing
3rd rowcomputing
4th rowcomputing
5th rowcomputing

Common Values

ValueCountFrequency (%)
business and management19764
 
12.5%
engineering9962
 
6.3%
computing9881
 
6.2%
nursing and midwifery8666
 
5.5%
allied health7666
 
4.8%
creative arts and design7641
 
4.8%
biosciences7047
 
4.4%
performing arts6696
 
4.2%
English studies6048
 
3.8%
sociology, social policy and anthropology5643
 
3.6%
Other values (25)69686
43.9%

Length

2025-12-03T16:22:14.081486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and89341
21.0%
studies19980
 
4.7%
business19764
 
4.6%
management19764
 
4.6%
arts14337
 
3.4%
health12769
 
3.0%
sciences11664
 
2.7%
social10746
 
2.5%
engineering9962
 
2.3%
computing9881
 
2.3%
Other values (56)206949
48.7%

Most occurring characters

ValueCountFrequency (%)
n320291
 
9.8%
e308168
 
9.5%
a296641
 
9.1%
i271664
 
8.3%
266457
 
8.2%
s240083
 
7.4%
o165103
 
5.1%
d160104
 
4.9%
t151006
 
4.6%
c150090
 
4.6%
Other values (17)929285
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3258892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n320291
 
9.8%
e308168
 
9.5%
a296641
 
9.1%
i271664
 
8.3%
266457
 
8.2%
s240083
 
7.4%
o165103
 
5.1%
d160104
 
4.9%
t151006
 
4.6%
c150090
 
4.6%
Other values (17)929285
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3258892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n320291
 
9.8%
e308168
 
9.5%
a296641
 
9.1%
i271664
 
8.3%
266457
 
8.2%
s240083
 
7.4%
o165103
 
5.1%
d160104
 
4.9%
t151006
 
4.6%
c150090
 
4.6%
Other values (17)929285
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3258892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n320291
 
9.8%
e308168
 
9.5%
a296641
 
9.1%
i271664
 
8.3%
266457
 
8.2%
s240083
 
7.4%
o165103
 
5.1%
d160104
 
4.9%
t151006
 
4.6%
c150090
 
4.6%
Other values (17)929285
28.5%
Distinct160
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.1 MiB
2025-12-03T16:22:14.148767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1745700
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAH11-01-06
2nd rowCAH11-01-06
3rd rowCAH11-01-06
4th rowCAH11-01-06
5th rowCAH11-01-06
ValueCountFrequency (%)
cah11-01-013617
 
2.3%
cah17-01-023429
 
2.2%
cah04-01-013402
 
2.1%
cah16-01-013348
 
2.1%
cah15-01-023159
 
2.0%
cah22-01-012889
 
1.8%
cah17-01-082889
 
1.8%
cah25-02-022835
 
1.8%
cah03-02-012781
 
1.8%
cah24-01-052754
 
1.7%
Other values (150)127597
80.4%
2025-12-03T16:22:14.241525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0385274
22.1%
-317400
18.2%
1237998
13.6%
C158700
9.1%
A158700
9.1%
H158700
9.1%
2113041
 
6.5%
446412
 
2.7%
545413
 
2.6%
340284
 
2.3%
Other values (4)83778
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1745700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0385274
22.1%
-317400
18.2%
1237998
13.6%
C158700
9.1%
A158700
9.1%
H158700
9.1%
2113041
 
6.5%
446412
 
2.7%
545413
 
2.6%
340284
 
2.3%
Other values (4)83778
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1745700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0385274
22.1%
-317400
18.2%
1237998
13.6%
C158700
9.1%
A158700
9.1%
H158700
9.1%
2113041
 
6.5%
446412
 
2.7%
545413
 
2.6%
340284
 
2.3%
Other values (4)83778
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1745700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0385274
22.1%
-317400
18.2%
1237998
13.6%
C158700
9.1%
A158700
9.1%
H158700
9.1%
2113041
 
6.5%
446412
 
2.7%
545413
 
2.6%
340284
 
2.3%
Other values (4)83778
 
4.8%
Distinct160
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2025-12-03T16:22:14.304934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length35
Mean length18.778721
Min length3

Characters and Unicode

Total characters2980183
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomputer games and animation
2nd rowcomputer games and animation
3rd rowcomputer games and animation
4th rowcomputer games and animation
5th rowcomputer games and animation
ValueCountFrequency (%)
and32993
 
9.4%
studies20898
 
5.9%
non-specific19332
 
5.5%
sciences13095
 
3.7%
engineering11744
 
3.3%
nursing7127
 
2.0%
business6696
 
1.9%
management5886
 
1.7%
health5886
 
1.7%
in5697
 
1.6%
Other values (185)223333
63.3%
2025-12-03T16:22:14.395664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e291560
 
9.8%
i290807
 
9.8%
n270203
 
9.1%
s215590
 
7.2%
c207296
 
7.0%
a196684
 
6.6%
193987
 
6.5%
o174198
 
5.8%
t151678
 
5.1%
r134506
 
4.5%
Other values (30)853674
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2980183
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e291560
 
9.8%
i290807
 
9.8%
n270203
 
9.1%
s215590
 
7.2%
c207296
 
7.0%
a196684
 
6.6%
193987
 
6.5%
o174198
 
5.8%
t151678
 
5.1%
r134506
 
4.5%
Other values (30)853674
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2980183
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e291560
 
9.8%
i290807
 
9.8%
n270203
 
9.1%
s215590
 
7.2%
c207296
 
7.0%
a196684
 
6.6%
193987
 
6.5%
o174198
 
5.8%
t151678
 
5.1%
r134506
 
4.5%
Other values (30)853674
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2980183
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e291560
 
9.8%
i290807
 
9.8%
n270203
 
9.1%
s215590
 
7.2%
c207296
 
7.0%
a196684
 
6.6%
193987
 
6.5%
o174198
 
5.8%
t151678
 
5.1%
r134506
 
4.5%
Other values (30)853674
28.6%

question
Categorical

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.5 MiB
Q01: How good are teaching staff at explaining things?
 
5878
Q13: How often have you received assessment feedback on time?
 
5878
Q26: How well communicated was information about your university/college's mental wellbeing support services?
 
5878
Q24: How clear is it that students' feedback on the course is acted on?
 
5878
Q22: To what extent do you get the right opportunities to give feedback on your course?
 
5878
Other values (23)
129310 

Length

Max length116
Median length84
Mean length79.855388
Min length39

Characters and Unicode

Total characters12673050
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ01: How good are teaching staff at explaining things?
2nd rowQ02: How often do teaching staff make the subject engaging?
3rd rowQ03: How often is the course intellectually stimulating?
4th rowQ04: How often does your course challenge you to achieve your best work?
5th rowQ05: To what extent have you had the chance to explore ideas and concepts in depth?

Common Values

ValueCountFrequency (%)
Q01: How good are teaching staff at explaining things?5878
 
3.7%
Q13: How often have you received assessment feedback on time?5878
 
3.7%
Q26: How well communicated was information about your university/college's mental wellbeing support services?5878
 
3.7%
Q24: How clear is it that students' feedback on the course is acted on?5878
 
3.7%
Q22: To what extent do you get the right opportunities to give feedback on your course?5878
 
3.7%
Q21: How easy is it to access subject specific resources (e.g., equipment, facilities, software) when you need them?5878
 
3.7%
Q20: How well have the library resources (e.g., books, online services and learning spaces) supported your learning?5878
 
3.7%
Q19: How well have the IT resources and facilities supported your learning?5878
 
3.7%
Q18: How well were any changes to teaching on your course communicated?5878
 
3.7%
Q16: How well have teaching staff supported your learning?5878
 
3.7%
Other values (18)99920
63.0%

Length

2025-12-03T16:22:14.423599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
how128260
 
6.0%
your109575
 
5.1%
to98872
 
4.6%
you81239
 
3.8%
the78513
 
3.7%
course65707
 
3.1%
have58780
 
2.8%
well58777
 
2.8%
and51849
 
2.4%
on41146
 
1.9%
Other values (161)1355661
63.7%

Most occurring characters

ValueCountFrequency (%)
1987313
15.7%
e1305487
 
10.3%
o888166
 
7.0%
t784897
 
6.2%
s683480
 
5.4%
a666298
 
5.3%
n618837
 
4.9%
r537600
 
4.2%
i531715
 
4.2%
u455307
 
3.6%
Other values (40)4213950
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12673050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1987313
15.7%
e1305487
 
10.3%
o888166
 
7.0%
t784897
 
6.2%
s683480
 
5.4%
a666298
 
5.3%
n618837
 
4.9%
r537600
 
4.2%
i531715
 
4.2%
u455307
 
3.6%
Other values (40)4213950
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12673050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1987313
15.7%
e1305487
 
10.3%
o888166
 
7.0%
t784897
 
6.2%
s683480
 
5.4%
a666298
 
5.3%
n618837
 
4.9%
r537600
 
4.2%
i531715
 
4.2%
u455307
 
3.6%
Other values (40)4213950
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12673050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1987313
15.7%
e1305487
 
10.3%
o888166
 
7.0%
t784897
 
6.2%
s683480
 
5.4%
a666298
 
5.3%
n618837
 
4.9%
r537600
 
4.2%
i531715
 
4.2%
u455307
 
3.6%
Other values (40)4213950
33.3%

agree_pct
Real number (ℝ)

High correlation 

Distinct792
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.433394
Minimum0
Maximum100
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:14.449026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q177.4
median85.7
Q392
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)14.6

Descriptive statistics

Standard deviation12.280903
Coefficient of variation (CV)0.1471941
Kurtosis2.4849721
Mean83.433394
Median Absolute Deviation (MAD)7.1
Skewness-1.2603183
Sum13240880
Variance150.82059
MonotonicityNot monotonic
2025-12-03T16:22:14.475714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10012051
 
7.6%
802622
 
1.7%
902494
 
1.6%
90.92257
 
1.4%
83.32094
 
1.3%
85.71965
 
1.2%
751938
 
1.2%
91.71689
 
1.1%
87.51679
 
1.1%
81.81606
 
1.0%
Other values (782)128305
80.8%
ValueCountFrequency (%)
02
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
5.91
 
< 0.1%
6.31
 
< 0.1%
6.71
 
< 0.1%
81
 
< 0.1%
8.35
< 0.1%
9.16
< 0.1%
9.52
 
< 0.1%
ValueCountFrequency (%)
10012051
7.6%
99.77
 
< 0.1%
99.64
 
< 0.1%
99.59
 
< 0.1%
99.412
 
< 0.1%
99.316
 
< 0.1%
99.235
 
< 0.1%
99.117
 
< 0.1%
9934
 
< 0.1%
98.941
 
< 0.1%

agree_pct_sd
Real number (ℝ)

High correlation 

Distinct348
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.869937
Minimum0
Maximum45.6
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:14.501111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q14.2
median6.2
Q38.7
95-th percentile13.5
Maximum45.6
Range45.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.598817
Coefficient of variation (CV)0.52385008
Kurtosis4.0601699
Mean6.869937
Median Absolute Deviation (MAD)2.2
Skewness1.4250737
Sum1090259
Variance12.951484
MonotonicityNot monotonic
2025-12-03T16:22:14.527937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.72215
 
1.4%
42210
 
1.4%
4.82156
 
1.4%
4.52127
 
1.3%
4.12125
 
1.3%
3.82101
 
1.3%
4.32097
 
1.3%
4.92094
 
1.3%
5.32093
 
1.3%
4.22084
 
1.3%
Other values (338)137398
86.6%
ValueCountFrequency (%)
09
 
< 0.1%
0.31
 
< 0.1%
0.419
 
< 0.1%
0.58
 
< 0.1%
0.68
 
< 0.1%
0.715
 
< 0.1%
0.837
< 0.1%
0.942
< 0.1%
148
< 0.1%
1.177
< 0.1%
ValueCountFrequency (%)
45.61
 
< 0.1%
43.41
 
< 0.1%
42.71
 
< 0.1%
41.91
 
< 0.1%
41.41
 
< 0.1%
40.41
 
< 0.1%
39.63
< 0.1%
39.31
 
< 0.1%
38.71
 
< 0.1%
38.21
 
< 0.1%

benchmark
Real number (ℝ)

High correlation 

Distinct490
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.142716
Minimum46.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:14.553805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46.3
5-th percentile69.6
Q179.4
median84.3
Q387.8
95-th percentile92.7
Maximum100
Range53.7
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation6.9264701
Coefficient of variation (CV)0.083308201
Kurtosis1.0415599
Mean83.142716
Median Absolute Deviation (MAD)4.1
Skewness-0.88593371
Sum13194749
Variance47.975988
MonotonicityNot monotonic
2025-12-03T16:22:14.580229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.11285
 
0.8%
871251
 
0.8%
87.61250
 
0.8%
86.71203
 
0.8%
87.31192
 
0.8%
86.81188
 
0.7%
86.91183
 
0.7%
85.51172
 
0.7%
87.51170
 
0.7%
87.71166
 
0.7%
Other values (480)146640
92.4%
ValueCountFrequency (%)
46.31
< 0.1%
46.81
< 0.1%
47.21
< 0.1%
47.71
< 0.1%
48.81
< 0.1%
49.61
< 0.1%
49.91
< 0.1%
501
< 0.1%
50.61
< 0.1%
50.91
< 0.1%
ValueCountFrequency (%)
10051
< 0.1%
99.81
 
< 0.1%
99.73
 
< 0.1%
99.31
 
< 0.1%
99.23
 
< 0.1%
99.12
 
< 0.1%
997
 
< 0.1%
98.94
 
< 0.1%
98.84
 
< 0.1%
98.77
 
< 0.1%

respondents
Real number (ℝ)

High correlation 

Distinct614
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.308582
Minimum1
Maximum3589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:14.605028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q115
median29
Q362
95-th percentile170
Maximum3589
Range3588
Interquartile range (IQR)47

Descriptive statistics

Standard deviation82.205844
Coefficient of variation (CV)1.5715556
Kurtosis615.91684
Mean52.308582
Median Absolute Deviation (MAD)17
Skewness17.120398
Sum8301372
Variance6757.8008
MonotonicityNot monotonic
2025-12-03T16:22:14.631643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105511
 
3.5%
115042
 
3.2%
135003
 
3.2%
124331
 
2.7%
144007
 
2.5%
153886
 
2.4%
163602
 
2.3%
173436
 
2.2%
183089
 
1.9%
193067
 
1.9%
Other values (604)117726
74.2%
ValueCountFrequency (%)
15
 
< 0.1%
2162
 
0.1%
3565
 
0.4%
41223
 
0.8%
51998
 
1.3%
62552
1.6%
72469
1.6%
82454
1.5%
92474
1.6%
105511
3.5%
ValueCountFrequency (%)
35892
< 0.1%
35882
< 0.1%
35852
< 0.1%
35831
< 0.1%
35821
< 0.1%
35812
< 0.1%
35801
< 0.1%
35782
< 0.1%
35741
< 0.1%
35731
< 0.1%

population
Real number (ℝ)

High correlation 

Distinct416
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.837593
Minimum1
Maximum4733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2025-12-03T16:22:14.657718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q120
median40
Q385
95-th percentile244
Maximum4733
Range4732
Interquartile range (IQR)65

Descriptive statistics

Standard deviation113.71286
Coefficient of variation (CV)1.5829157
Kurtosis533.74741
Mean71.837593
Median Absolute Deviation (MAD)24
Skewness15.77976
Sum11400626
Variance12930.614
MonotonicityNot monotonic
2025-12-03T16:22:14.683393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163585
 
2.3%
173409
 
2.1%
153373
 
2.1%
123078
 
1.9%
142977
 
1.9%
132942
 
1.9%
202889
 
1.8%
212781
 
1.8%
192727
 
1.7%
222673
 
1.7%
Other values (406)128266
80.8%
ValueCountFrequency (%)
15
 
< 0.1%
2157
 
0.1%
3446
 
0.3%
4681
 
0.4%
51442
0.9%
61741
1.1%
71471
0.9%
81654
1.0%
91826
1.2%
102079
1.3%
ValueCountFrequency (%)
473327
< 0.1%
232227
< 0.1%
220627
< 0.1%
136027
< 0.1%
97027
< 0.1%
96727
< 0.1%
95127
< 0.1%
94127
< 0.1%
80027
< 0.1%
79627
< 0.1%

Interactions

2025-12-03T16:22:13.344530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.555196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.741543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.894612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.043242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.193907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.369614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.579082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.767449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.918981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.068027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.219511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.394179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.602126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.793062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.944753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.093046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.244474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.417050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.624688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.816989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.968866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.118269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.268995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.441017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.648901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.842392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.994101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.143320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.294033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.465572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.674253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:12.869125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.019352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.170511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T16:22:13.320367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-03T16:22:14.746681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agree_pctagree_pct_sdbenchmarkcah1_codecah1_subjectcah2_codecah2_subjectpopulationquestionrespondentsukprn
agree_pct1.000-0.1970.5380.0490.0490.0580.058-0.1060.161-0.0830.008
agree_pct_sd-0.1971.000-0.2870.0980.0980.1200.120-0.9010.083-0.9190.018
benchmark0.538-0.2871.0000.1120.1120.1390.139-0.0210.377-0.0110.026
cah1_code0.0490.0980.1121.0001.0001.0001.0000.0640.0000.0510.166
cah1_subject0.0490.0980.1121.0001.0001.0001.0000.0640.0000.0510.166
cah2_code0.0580.1200.1391.0001.0001.0001.0000.0700.0000.0570.172
cah2_subject0.0580.1200.1391.0001.0001.0001.0000.0700.0000.0570.172
population-0.106-0.901-0.0210.0640.0640.0700.0701.0000.0000.988-0.014
question0.1610.0830.3770.0000.0000.0000.0000.0001.0000.0000.000
respondents-0.083-0.919-0.0110.0510.0510.0570.0570.9880.0001.000-0.022
ukprn0.0080.0180.0260.1660.1660.1720.172-0.0140.000-0.0221.000

Missing values

2025-12-03T16:22:13.515083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-03T16:22:13.591250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ukprnprovidercah1_codecah1_subjectcah2_codecah2_subjectcah3_codecah3_subjectquestionagree_pctagree_pct_sdbenchmarkrespondentspopulation
010042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ01: How good are teaching staff at explaining things?87.42.887.0119141
110042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ02: How often do teaching staff make the subject engaging?80.53.670.6118141
210042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ03: How often is the course intellectually stimulating?77.83.581.7117141
310042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ04: How often does your course challenge you to achieve your best work?84.93.283.0119141
410042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ05: To what extent have you had the chance to explore ideas and concepts in depth?80.53.580.0118141
510042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ06: How well does your course introduce subjects and skills in a way that builds on what you have already learned?79.03.479.5119141
610042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ07: To what extent have you had the chance to bring together information and ideas from different topics?82.23.478.6118141
710042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ08: To what extent does your course have the right balance of directed and independent study?70.63.873.9119141
810042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ09: How well has your course developed your knowledge and skills that you think you will need for your future?88.13.180.8118141
910042570AAP Education LimitedCAH11computingCAH11-01computingCAH11-01-06computer games and animationQ10: How clear were the marking criteria used to assess your work?61.34.073.1119141
ukprnprovidercah1_codecah1_subjectcah2_codecah2_subjectcah3_codecah3_subjectquestionagree_pctagree_pct_sdbenchmarkrespondentspopulation
15869010007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ18: How well were any changes to teaching on your course communicated?90.010.082.81012
15869110007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ19: How well have the IT resources and facilities supported your learning?100.010.188.91012
15869210007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ20: How well have the library resources (e.g., books, online services and learning spaces) supported your learning?100.09.192.41012
15869310007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ21: How easy is it to access subject specific resources (e.g., equipment, facilities, software) when you need them?90.010.889.61012
15869410007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ22: To what extent do you get the right opportunities to give feedback on your course?90.08.289.91012
15869510007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ23: To what extent are students' opinions about the course valued by staff?100.011.384.11012
15869610007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ24: How clear is it that students' feedback on the course is acted on?55.615.366.3912
15869710007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ25: How well does the students' union (association or guild) represent students' academic interests?50.018.973.4612
15869810007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ26: How well communicated was information about your university/college's mental wellbeing support services?90.011.580.41012
15869910007713York St John UniversityCAH26geography, earth and environmental studiesCAH26-01geography, earth and environmental studiesCAH26-01-02physical geographical sciencesQ27: During your studies, how free did you feel to express your ideas, opinions, and beliefs?100.08.089.41012